Sensing and Control of Additive Manufacturing Processes

ABSTRACT

Systems, devices, and methods for additive manufacturing are provided that allow for components being manufactured to be assessed during the printing process. As a result, changes to a print plan can be considered, made, and implemented during the printing process. More particularly, in exemplary embodiments, a spectrometer is operated while a component is being printed to measure one or more parameters associated with one or more layers of the component being printed. The measured parameter(s) are then relied upon to determine if any changes are needed to the way printing is occurring, and if such changes are desirable, the system is able to implement such changes during the printing process. By way of non-limiting examples, printed material in one or more layers may be reheated to alter the printed component, such as to remove defects identified by the spectrometer data. A variety of systems, devices, and methods for performing real-time sensing and control of an additive manufacturing process are also provided.

CROSS REFERENCE TO RELATED APPLICATION

The present application claims priority to and the benefit of U.S.patent application Ser. No. 16/114,188, filed Aug. 27, 2018, andentitled “Sensing and Control of Additive Manufacturing Processes,”which claims priority to and the benefit of U.S. Provisional ApplicationNo. 62/550,209, filed Aug. 25, 2017, and titled “Sensing and Control ofAdditive Manufacturing Processes,” the contents of each which is herebyincorporated by reference in its entirety.

GOVERNMENT RIGHTS

This invention was made with Government support under Grant No.N000189586 awarded by the Department of Energy. The Government hascertain rights in the invention.

FIELD

The present disclosure relates to systems, devices, and methods foradditive manufacturing, and more particularly relates to providingsensing and control during the course of printing a three-dimensionalobject, such as by obtaining accurate temperature readings ofmaterial(s) involved in printing prior to, during, and/or afterdepositing the material(s) during a manufacturing process.

BACKGROUND

Additive Manufacturing (AM) has become a central technology for rapidprototyping and short run manufacturing. One advantage of AM lies in itsability to create geometries inaccessible with conventionalmanufacturing techniques. This immense flexibility is challenged,however, by problems related to, by way of non-limiting examples,defects, surface roughness, and residual stress, which can limit use ofAM-printed objects in high-value or mission critical applications. Thismay only be overcome by fabricating parts that meet stringentqualification standards, e.g., for use in aerospace, medicine, andenergy generation applications. Presently, high performance AM parts areinspected after fabrication on the part-by-part basis using conventionalmetrology and non-destructive testing (NDT) methods. It can often takebetween about four hours to about eight hours per part to confirm itsaccuracy before it can be used. Further, to the extent a printed partfails inspection, the whole part may have to be disposed of, and it cantake more time, effort, energy, and money, to go back and start theprinting from the beginning. However, costly evaluation is necessary dueto the extreme and poorly understood process variability present acrossall practical AM technologies. Components produced on the same machine,using the same build parameters, often perform differently in service.As a result, it has so far proven difficult, if not impossible, tocertify both an AM component design and process for production of highquality parts.

Accordingly, there is a need for AM systems, devices, and methods thatallow for an AM component to be inspected, certified, and/or verified inreal time, i.e., while the AM component is being manufactured. Thisshould allow for components, and particularly high quality parts havinghigh-value and/or mission critical applications, to be more accuratelyand efficiently printed, without wasting materials and parts because thecomponents failed some inspection, certification, or verification atsome later point in time.

SUMMARY

The systems, devices, and methods described herein address the problemsof process control (PC) and quality control (QC) in AM, specificallythrough in-situ, spectrally and spatially resolved interrogation of theprocess. As provided for in the present disclosure, a time-varyingspectral signature of a component may be used to modulate processparameters, such signatures including a rate of energy deposition, ascan rate, and infill scan patterns, which in turn can be used to affectparameters such as finished component accuracy, microstructure, residualstress distribution, and surface finish. This closed-loop processcontrol provides a means for reducing the variability in the propertiesof the resulting components. Moreover, in-situ monitoring of the buildprocess provides a means for quality assessment of the completed parts.As provided for herein, parameters such as porosity, surface roughness,melt pool contamination, and anomalous temperature history can all bequantified and controlled in conjunction with the disclosed systems,devices, and methods to provide better PC and QC. Accuratetime-temperature data also can enable more sophisticated QC methods,such as a novel implementation of thermal diffusivity tomography. Stillfurther, there are distinct advantages to QC measures that may beperformed in a layerwise fashion as provided for in the presentdisclosure. For example, minor defects can be remediated by localizedremelting, complex interior features may be interrogated before theybecome inaccessible, and the build may even be terminated prematurely ifirrecoverable defects are found. Such measures are not necessarilytemperature-dependent, and, in view of the present disclosure and theknowledge of a person skilled in the art, other advantages can berealized in view of the provided for QC measures, whether those measuresinvolve temperature measurements, spectrally resolved radiancemeasurements, or otherwise.

Practically implementing these sensing technologies typically involvesnon-trivial integration of high-speed, spectrally resolved opticalsensing, processing to extract actionable information from the generateddata, data processing to determine the desired process parameters,and/or an interface by which this control is managed. A potential,non-limiting embodiment is illustrated in FIG. 1 below, in which animaging spectrometer is used to interrogate a selective laser melting(SLM) additive manufacturing process. Such a spectrometer devicenatively produces hypercubes, providing data as a function of bothspatial position in the scene (X and Y) and a third dimension thatresolves wavelength (A). These data may be processed to extract otherdata products, such as temperature profiles, which may be used fordirect control of an AM process.

In one exemplary embodiment of a system for manufacturing athree-dimensional object, the system includes an additive manufacturing(AM) printer, an imaging spectrometer, and a controller. The AM printeris configured to fuse or deposit a plurality of layers to manufacture athree-dimensional (3D) object according to a build plan (also referredto as a print plan). The imaging spectrometer is configured to measureone or more parameters while the plurality of layers are fused ordeposited by the AM printer, as well as transmit one or more signalsthat correlate to the measured parameter(s). The controller isconfigured to receive the signals that correlate to the measuredparameter(s), determine if any changes to the build plan are desirablein view of the measured parameter(s), and, if changes are determined tobe desirable, adjust the build plan in view of the measured parameter(s)while the AM printer is still in the process of manufacturing the 3Dobject.

The build plan can involve a number of actions, steps, and parametersassociated with performing such actions and steps. By way ofnon-limiting example, the build plan can include one or more of a scanspeed, a laser power, a laser scan path, a spot size, and a rate ofheating or cooling a material(s) that is fused or deposited in one ormore layers of the plurality of layers being fused or deposited by theAM printer. In such embodiments, an adjustment of the build plan by thecontroller includes at least one of adjusting the scan speed, the laserpower, the toolpath, the spot size, or the rate of heating or cooling amaterial(s) being fused or deposited in one or more layers of theplurality of layers in response to the measured parameter(s).

Any number of parameters can be measured. By way of non-limitingexample, such parameters can include one or more of a temperaturedistribution, emissivity, spectrally resolved radiance measurements,band ratios, radiation transport characteristics, and a melt pool shape.Adjustment made by the controller can then be based on the parameter(s)that is measured. The controller can be configured to determinestatistical moments of the parameter(s), such as averages or variances,along spatial, temporal, or spectral dimensions of the recorded data.For example, mean melt pool temperature may be used to adjust laserpower, and the variance of that temperature over time provides a metricfor process stability. In alternate embodiments, the controller can beconfigured to extract at least one of spatial derivatives, temporalderivatives, or spectral derivatives and process such derivative(s) togenerate a quality control assessment and/or process control signal. Forexample, measured thermal gradients across the melt pool enable one tooptimally tune laser power, spot size, and energy profile (energydelivered as a function of location within the laser spot, which mayincorporate Gaussian, quasi-Gaussian, top-hat, or donut character, byway of non-limiting examples). At the boundary of a component,dimensional accuracy may be improved by maximizing the spatial thermalgradient, such that the edge of the part is sharply defined. Conversely,a more diffuse shape for the interior of a component may help improvefusion of molten material to the component. Gradient is used herein todescribe a profile or derivative of a variable along one or moredimensions of a dataset, regardless of the dimensions thereof. Forexample, the total derivative of a temperature with respect to X and Yposition, and the 1D derivative of center melt pool temperature as afunction of time both comprise a gradient.

The controller can be configured to perform a variety of otherfunctions, separate from, or in conjunction with, the determining andadjusting functions. By way of non-limiting example, the controller canbe configured to perform temperature-emissivity separation of themeasured parameter(s), and the measured parameter(s) can include one ormore spectrally resolved radiance measurements. In some suchembodiments, the spectrally resolved radiance measurement(s) can includea first spectrally resolved radiance measurement and a second spectrallyresolved radiance measurement, and the temperature-emissivity separationcan be performed using a two temperature method. In some other suchembodiments, the measured parameter(s) can include a spectrally resolvedradiance measurement, an in-band emissivity of the 3D object can bedesignated as an arbitrary value that is identical for a single pair ofspectral bands that include the spectrally resolved radiancemeasurement, and the temperature-emissivity separation can be performedusing a grey body method.

The measured parameter(s) can include emissivity data. In some suchembodiments, the controller can be configured to qualify the dimensionsof the 3D object in view of the emissivity data. Alternatively, oradditionally, the measured parameter(s) can include anomalous spectralfeatures. In some such embodiments, the controller can be configured toassess parameters of a melt pool of the AM printer in view of theanomalous spectral features.

The measured parameter(s) can be sufficient to allow the controller todetect one or more defects in the plurality of fused or depositedlayers. In such instances, the controller can be configured to adjustthe build plan such that at least one defect of the one or more defectsis remediated by the controller adjusting the build plan to allow for aportion of the plurality of fused or deposited layers to remelt materialincluded as part of the portion of the plurality of fused or depositedlayers. In some such embodiments, controller being configured to performat least one of the following: use one or more recovered control signalsto quantify defect morphology; deliver extra feedstock to the defectlocation; calculate optimal scan parameters in view of defect andfeedstock properties; or apply one or more scan parameters to the buildplan.

Other examples of adjustments to the build plan that can be made by thecontroller include at least one of: (1) altering one or more laser scanparameters of the AM printer to achieve at least one of a desirablethermal gradient or a time derivative temperature within a definedregion of the three-dimensional object (i.e., the build surface); (2)selectively re-heating at least a portion of at least one layer of theplurality of layers to achieve at least one of a desirable thermalprofile, thermal gradient, or a time derivative of temperature within adefined region of the three-dimensional object (i.e., the buildsurface); and (3) adjusting a temperature of at least one of a melt poolof the AM printer or at least a portion of at least one layer of theplurality of layers to alter at least one of a microstructure, residualstress, or surface roughness of a defined region of the 3D object.Achieving a desirable thermal profile can be as simple as heating aportion of the layer to a target temperature, i.e., melting it. Stillfurther non-limiting examples include adjusting an energy profile of afirst layer of the AM printer to maintain a desired temperature gradientabout a portion of one or more lasers of the plurality of lasers that ismelted by a second laser of the AM printer.

The controller can be configured to adjust an energy profile of one ormore process lasers of the AM printer using at least one of adaptivefeed-forward control or feedback control. Extracted control signals areable to be the input to such a controller to adjust process inputs.Given the repetitive nature of layerwise AM processes, process controlmay occur on several timescales. At one extreme, machine parameters canbe updated on-the-fly, with the aim of mitigating process variations.For example, laser energy may be tuned in response to actual melt pooltemperature and size as it fuses the material, thereby reducing thedegree to which process instabilities manifest as defects in thecompleted component. A typical scan speed for SLM processing can be onthe order of about 250 millimeters per second, and melt pool diameterscan often be on the order of about 100 μm. In such instances, samplingthe melt pool as it moves 1/10^(th) of this characteristic dimension canrequire sampling at about 25 kHz. At this rate, modern processors can becapable of performing 100 million floating point operations betweensubsequent frames, or sufficiently rapidly to extract many controlsignals of interest and update machine parameters prior to the nextsample. Issues concerning data transfer latency at these speeds can beoptimally addressed by directly coupling the sensor to the processor;this is commonly seen in state-of-the-art high-speed imaging equipment,in which an image sensor, a field-programmable gate array (FPGA), andmemory are directly coupled.

Updates to machine parameters also may be performed at longertimescales, either in addition to the methods discussed in the precedingparagraph, or separately. In some instances, data recorded during aprevious laser pulse(s), scan vector(s), layer(s), and/or entirecomponents can be used to update process parameters for material to befused. For example, consider this process implemented vector-wise forthe first overhanging layer of a “F” shaped component, in which thelaser is swept from left to right in parallel passes over the entirelength of the component. The first scan vector may show optimal processparameters for the supported region on the left, however, this energydensity can result in overheating of the unsupported region if performedwithout the real-time feedback mechanism of the preceding paragraph.However, after sensing the region of overheating along this firstvector, the controller can compensate by reducing laser power for thecorresponding segment of the second pass that lies adjacent to theoverhanging regions of the first. More generally, data collected for anyregion near the vector to be scanned may be used to update processparameters. If a region under the vector to be scanned features alack-of-fusion type defect, a capable controller may increase laserpower or decrease scan speed for the vector segment over this region,thereby remelting the lower layer and eliminating the defect. Similarly,this can be anticipated in advance of the scan, for example, by analysisof the geometry of the component to determine an initial set of processparameters, and the real-time control can further refine the processparameters to achieve the target performance.

Further, this process, and those related thereto or otherwise derivablefrom the present disclosure, may be adapted to more complex scanstrategies. For example, many SLM parts can be fabricated using anisland pattern, in which the interior of the component is scanned in acheckerboard-like pattern. Each tile can typically be between about 2millimeters and about 10 millimeters square, scanned, for example, as aseries of parallel lines. In this case, data from neighboring tiles,including those in the previous layer, may be used to adjust processparameters. Moreover, because each tile can be comprised of a series ofparallel lines, the process may also leverage data from adjoining scanlines, as outlined above. As a further alternative, some components canbe fabricated using a series of pulses at discrete points instead ofsweeping a continuously operating laser over the build material. Theprocess of the present disclosure proceeds in much the same case in thiscondition; process measurements for neighboring points, again includingthose in the preceding layer, can be fed-forward to more optimally setprocess parameters for subsequent points.

Still further, this process, and those related thereto or otherwisederivable from the present disclosure, can operate off-line. In suchcases, multispectral radiographic data can be collected for thefabrication of a first component, and can be used to update processparameters for the fabrication of a second component. Returning again tothe “F” component example, overheating of the overhanging region can beobserved for the first component. Altering the laser power in themachine code for the second fabricated component in this region canresult in superior surface finish and form accuracy. Operating on thistimescale may have the benefit of immunity to processing latency, andeffectiveness in mitigating systematic problems, such as those arisingfrom component geometry and machine misalignment or miscalibration.However, unlike the methods above, such a system may not effectivelyaddress variations in part quality arising from stochastic variabilityin the SLM process, nor may it be effective to improve the quality ofcomponents produced individually.

In some such instances, the controller can be configured to adjust theenergy profile of the process laser(s) of the AM printer usingpreviously recorded process data. Alternatively, or additionally, thecontroller can be configured to adjust the energy profile of the processlaser(s) of the AM printer using a CAD code and/or analysis of thecorresponding machine code to generate control signals. Returning to anoverhanging feature example, the controller can decrease laser power atthe transition from printing above previously-solidified material toprinting above powder to compensate for less effective thermal transferout of the melt pool, with fine adjustment being able to be provided viafeedback control. In some embodiments, the controller can be configuredto terminate manufacture of the 3D object prior to completion of thebuild plan in response to a defect detected by way of the measuredparameter(s).

In one exemplary method, the method includes depositing or fusing one ormore layers of a plurality of layers of a three-dimensional (3D) object,with the depositing or fusing being performed by an additivemanufacturing (AM) printer. The method further includes measuring one ormore parameters associated with the one or more layers using an imagingspectrometer, with the measuring occurring prior to all of the layers ofthe plurality of layers of the 3D object being deposited or fused. Themeasured parameter(s) are communicated to a controller for processing,and such parameter(s) are processed using the controller to determine ifany adjustments to the deposited or fused layer(s) are desirable. If thecontroller determines one or more adjustments to the deposited or fusedlayer(s) are desirable, the method further includes communicatingcommands to the AM printer to institute the one or more adjustments tothe deposited or fused layer(s) prior to depositing or fusing all layersof the plurality of layers of the 3D object. The communication of thesecommands, for example, can be after the measurement and processing ofthe data from one or more layers occurs. The commands can includeupdating the instructions for a subsequent layer and/or applying adedicated correction process, among others known to those skilled in theart in view of the present disclosures. Further, different processcontrol techniques can occur on different timescales. For example, aclosed loop control of melt pool parameters, as provided for herein, canhappen in real time, while updating scan parameters for a current layerusing only data from the previous layer(s) can happen on any timescale,e.g., the process can wait for computation to finish.

Various commands can be communicated to the AM printer to institute theadjustment(s) to the deposited or fused layer(s) prior to depositing orfusing all layers of the plurality of layers of the 3D object. By way ofnon-limiting example, in some embodiments, the communicating step caninclude adjusting at least one of a scan speed, a laser power, a laserscan path, a spot size, or a rate of heating or cooling a material ofthe deposited or fused layer(s). Likewise, various parameters can bemeasured, and thus, non-limiting examples of the one or more measuredparameters can include at least one of a temperature distribution,emissivity, spectrally resolved radiance measurements, band ratios,radiation transport parameters, or a melt pool shape. In instances inwhich the measured parameter(s) includes emissivity data, the method caninclude qualifying the dimensions of the 3D object in view of theemissivity data. In instances in which the measured parameter(s)includes anomalous spectral features, the method can include assessingparameters of a melt pool of the AM printer in view of the anomalousspectral features.

Processing the measured parameter(s) using the controller to determineif any adjustments to the deposited or fused layer(s) are desirable caninclude at least one of: determining statistical moments of the one ormore parameters, extracting at least one of spatial derivatives,temporal derivatives, or spectral derivatives, and processing therespective derivative(s) to generate at least one of a quality controlassessment or a process control signal of the deposited or fusedlayer(s). The statistical moments can include a spatial moment, atemporal moment, and/or a spectral moment, among others, and in suchinstances, processing can include processing at least one of the spatialmoment, the temporal moment, the spectral moment, or the respectivederivative(s).

The method can further include performing temperature-emissivityseparation on the measured parameter(s), and the measured parameter(s)can include one or more spectrally resolved radiance measurements. Forexample, when the spectrally resolved radiance measurement(s) includes afirst spectrally resolved radiance measurement and a second spectrallyresolved radiance measurement, the temperature-emissivity separation canbe performed using a two temperature method. By way of alternativeexample, when the measured parameter(s) includes a spectrally resolvedradiance measurement, an in-band emissivity of the 3D object can bedesignated as an arbitrary value that is identical for a single pair ofspectral bands that include the spectrally resolved radiancemeasurement, and the temperature-emissivity separation can be performedusing a grey body method.

The step of communicating commands to the AM printer to institute theadjustment(s) to the deposited or fused layer(s) prior to depositing orfusing all layers of the plurality of layers of the 3D object caninclude altering one or more laser scan parameters of the AM printer toachieve at least one of a desirable thermal gradient and/or a timederivative temperature within a defined region of the three-dimensionalobject (i.e., the build surface). Additionally, or alternatively, thestep can include selectively re-heating at least a portion of thedeposited or fused layer(s) to achieve at least one of a desirablethermal profile, thermal gradient, and/or a time derivative oftemperature within a defined region of the three-dimensional object(i.e., the build surface). Achieving a desirable thermal profile can beas simple as heating a portion of the layer to a target temperature,i.e., melting it. Further additionally, or alternatively, the step caninclude adjusting a temperature of at least one of a melt pool of the AMprinter or at least a portion of the deposited or fused layer(s) toalter at least one of a microstructure, residual stress, or surfaceregion of a defined region of the 3D object.

In some embodiments, the step of communicating commands to the AMprinter to institute the adjustment(s) to the deposited or fusedlayer(s) prior to depositing or fusing all layers of the plurality oflayers of the 3D object can include adjusting an energy profile of afirst laser of the AM printer to maintain a desired temperature gradientabout a portion of the deposited or fused layer(s) that is melted by asecond laser of the AM printer. Further, in some embodiments, the stepof communicating commands to the AM printer to institute theadjustment(s) to the deposited or fused layer(s) prior to depositing orfusing all layers of the plurality of layers of the 3D object caninclude controlling at least one of a power or a scan pathway of a laserof the AM printer to provide substantially constant temperature along adesired region of a melt pool of the additive manufacturing printer as abeam of the AM printer scans. Still further, in some embodiments, thestep of communicating commands to the AM printer to institute theadjustment(s) to the deposited or fused layer(s) prior to depositing orfusing all layers of the plurality of layers of the 3D object caninclude performing at least one of adaptive feed-forward control orfeedback control to adjust an energy profile of one or more processlasers of the AM printer. In some such embodiments, at least one ofadaptive feed-forward control or feedback control uses previouslyrecorded process data to perform the control. Additionally, oralternatively, the method can include operating at least one of CAD codeor machine code to generate control signals in conjunction withadjusting the energy profile of the process laser(s) of the AM printer.

Communicating commands to the AM printer to institute the adjustment(s)to the deposited or fused layer(s) prior to depositing or fusing alllayers of the plurality of layers of the 3D object can include remeltingmaterial included as part of the deposited or fused layer(s). In someembodiments, the communicating commands step can include terminatingmanufacture of the 3D object prior to depositing or fusing all layers ofthe plurality of layers of the 3D object in response to a defectdetected by way of the measured parameter(s). The device that processesimage data may also be the device that controls the printer and/or theprinter itself. For example, image processing and printer control canboth be performed on the same FPGA.

Although embodiments described above and otherwise provided for hereininclude a controller, the systems, devices, and methods provided for inthe present disclosure by no means require a feedback loop configurationin which data (e.g., parameters) that is measured, sensed, or otherwiseacquired is subsequently relied upon to operate a controller to takesome later action. In some instances, the data can be recorded orotherwise stored while not being actively used. The data can be used atsome later point in time, whether for informational purposes, to makelong-term assessments related to performance, or for some other reasonrecognizable by a person skilled in the art, including use in generatinginspection reports or generation of a digital twin model.

To that end, in one exemplary embodiment of a system for manufacturing athree-dimensional object, the system includes an additive manufacturingprinter that is configured to fuse or deposit a plurality of layers tomanufacture a three-dimensional object, and an imaging spectrometerconfigured to measure one or more parameters while the plurality oflayers are fused or deposited by the additive manufacturing printer andtransmit one or more signals that correlate to the one or more measuredparameters so that one or more measured parameters is recorded. The oneor more measured parameters can include at least one of a temperaturedistribution, emissivity, band ratios, radiation transportcharacteristics, or a melt pool shape. In some embodiments, the measuredparameter(s) can include a first spectrally resolved radiancemeasurement and a second spectrally resolved radiance measurement. Thetwo measurements can be used in conjunction with performing atemperature-emissivity separation using a two temperature method torecover two temperatures corresponding to the two radiance measurementsand a spectrally resolved emissivity. In some other embodiments, themeasured parameter(s) can include a spectrally resolved radiancemeasurement. An in-band emissivity of the three-dimensional object canbe designated as an arbitrary value that is identical for a single pairof bands that include the spectrally resolved radiance measurement, anda temperature-emissivity separation can be performed using a grey bodymethod.

The three-dimensional object that is being built can be built inaccordance with a build plan. The build plan can include informationthat can change how the object is built. For example, the build plan caninclude at least one of a scan speed, a laser power, a laser scan path,a spot size, or a rate of heating or cooling a material being fused ordeposited in one or more layers of the plurality of layers. If a buildplan is adjusted in view of parameters measured or otherwise obtained bythe spectrometer, one or more of the aforementioned aspects of the buildplan (e.g., a scan speed, a laser power, a laser scan path, a spot size,or a rate of heating or cooling a material being fused or deposited inone or more layers of the plurality of layers) can be adjusted.

The methods for additive manufacturing can likewise record or otherwisestore the data while not actively using it. In one exemplary embodimentof a method for additive manufacturing, the method includes depositingor fusing one or more layers of a plurality of layers of athree-dimensional object, with the depositing or fusing being performedby an additive manufacturing printer, measuring one or more parametersassociated with the one or more layers using an imaging spectrometer,and communicating the one or more measured parameters to a storage unit.The measuring of the one or more parameters occurs prior to depositingor fusing all layers of the plurality of layers of the three-dimensionalobject.

The one or more measured parameters can include at least one of atemperature distribution, emissivity, band ratios, radiation transportcharacteristics, or a melt pool shape. In some embodiments, the methodcan include performing temperature-emissivity separation on the one ormore measured parameters. In some such embodiments, the measuredparameter(s) can include a first spectrally resolved radiancemeasurement and a second spectrally resolved radiance measurement, andthe temperature-emissivity separation can be performed using a twotemperature method. In some other such embodiments, the measuredparameter(s) can include a spectrally resolved radiance measurement, anin-band emissivity of the three-dimensional object can be designated asan arbitrary value that is identical for a single pair of spectral bandsthat include the spectrally resolved radiance measurement, and thetemperature-emissivity separation can be performed using a grey bodymethod.

The three-dimensional object that is being built can be built inaccordance with a build plan. The build plan can include informationthat can change how the object is built. In some such embodiments, thebuild plan can include at least one of a scan speed, a laser power, alaser scan path, a spot size, or a rate of heating or cooling a materialbeing fused or deposited in one or more layers of the plurality oflayers. The method can then include adjusting the build plan in view ofparameters measured or otherwise obtained by the spectrometer. Aspectsof the build plan that can be adjusted include a scan speed, a laserpower, a laser scan path, a spot size, or a rate of heating or cooling amaterial being fused or deposited in one or more layers of the pluralityof layers.

Adjustments that can be made during the printing process include, by wayof non-limiting example: controlling at least one of a power or a scanpathway of a laser of the additive manufacturing printer to providesubstantially constant temperature along a desired region of a melt poolof the additive manufacturing printer as a beam of the additivemanufacturing printer scans; or performing at least one of adaptivefeed-forward control or feedback control to adjust an energy profile ofone or more process lasers of the additive manufacturing printer. Ininstances in which adaptive feed-forward control and/or feedback controlis used, at least one of those controls can use previously recordedprocess data to perform the control.

In another exemplary embodiment of a system of additive manufacturing(AM), the system includes a three-dimensional (3D) printer, an imagingspectrometer, and an information processing device. The system, forinstance by way of the 3D printer, is capable of real-time control ofscan parameters, such as scan speed, laser power, and spot energydistribution for real-time process control, among other scan parameters.The control can be informed by data measured by the imaging spectrometerand processed by the information processing device.

An exemplary embodiment of a method of fabricating a component using thesystem provided for in the paragraph directly above can includerecording multispectral image data of the AM process, and processing thedata using a computational device to extract control signals. Thecomputational device can be, or can be associated with, the informationprocessing device of the system.

The process parameters can be updated in real-time in response tocontrol signals, including but not limited to melt pool temperaturedistribution, emissivity, band ratios, other radiation transportcharacteristics, and melt pool shape. In some instances, processparameters such as temperature, emissivity, band ratios, other RTfeatures, and melt pool shape, their statistical moments, and theirspatial and temporal derivatives can be extracted and processed for aquality control assessment.

The step of processing the data using a computational device to extractcontrol signals can include a temperature-emissivity separation step. Insome embodiments, extracted emissivity data can be used to qualifycomponent dimensions. Anomalous spectral features can be used to assessmelt pool contamination, such as oxidation.

An exemplary method of fabricating a component can include using thesystem provided for in paragraph [0028] above (the paragraph starting“In another exemplary embodiment of a system of additive manufacturing),the process provided for thereafter in paragraph [0029] (the paragraphstarting “An exemplary embodiment of a method of fabricating a componentusing the system provided for in the paragraph directly above), and,optionally, one or more of the processes provided for in paragraphs[0030] and [0031] (the paragraphs starting “The process parameters canbe updated” and “The step of processing the data”), as well as alteringscan parameters or selective re-heating to achieve a desirable thermalprofile and/or thermal gradients and/or time derivatives, manipulatingtemperature(s) to alter component microstructure, and/or manipulatingtemperature(s) to alter component residual stresses. Achieving adesirable thermal profile can be as simple as heating a portion of thelayer to a target temperature, i.e., melting it. Other methods caninclude the system (e.g., paragraph [0028]), the process (e.g.,paragraph [0029]), and, optionally, one or more additional processes(e.g., paragraphs [0030] and [0031]), as well as manipulating the energyprofile of a per/post heat laser in direct response to processconditions such that a preferential temperature gradient is maintainedin the region about the region melted by a second laser.

Still further, other methods can include the system (e.g., paragraph[0028]), the process (e.g., paragraph [0029]), and, optionally, one ormore additional processes (e.g., paragraphs and [0031]), as well ascontrolling the power or scan rate of a laser on a build surface to givea substantially constant temperature along a desired region of a meltpool of the AM printer as a beam of the AM printer scans, despitevariabilities in deposited or fused material, thermal properties of theunderlying material, and local temperature fluctuations. In suchinstances, real-time temperature-based feedback of the laser can enablethe reduction of temperature and/or shape fluctuations along a melttrack, and/or eliminate overheating along/near edges and corners. Stillother methods can include the system (e.g., paragraph [0028]), theprocess (e.g., paragraph) [0029]), and, optionally, one or moreadditional processes (e.g., paragraphs [0030] and [0031]), as welladjusting an energy profile of the process laser(s) using a combinationof adaptive feed-forward and feedback control using previously recordedprocess data, optionally in conjunction with CAD and/or machine code togenerate control signals.

Other methods can include the system (e.g., paragraph [0028]), theprocess (e.g., paragraph [0029]), and, optionally, one or moreadditional processes (e.g., paragraphs [0030] and) [0031]), as wellgenerating machine instructions to remediate detected defects, such asby remelting. Still further, other methods can include the system (e.g.,paragraph [0028]), the process (e.g., paragraph [0029]), and,optionally, one or more additional processes (e.g., paragraphs [0030]and [0031]), as well as terminating component fabrication in response toa detected defect.

Although references are made to particular systems and processes aboveby paragraph number, those references are merely examples of systems andprocesses that can be used. A person skilled in the art will recognizethe many other systems and processes provided for herein, or otherwisederivable from the present disclosures, can also be used in place of theexample systems and processes without departing from the spirit of thepresent disclosure. Reference to a particular system or process is by nomeans limiting, as the present disclosure provides for many systems andprocesses that can be used in place of particularly referenced systemsand methods.

BRIEF DESCRIPTION OF DRAWINGS

This disclosure will be more fully understood from the followingdetailed description taken in conjunction with the accompanyingdrawings, in which:

FIG. 1 is a schematic illustration of one exemplary embodiment of asystem for manufacturing a three-dimensional object;

FIG. 2 is a graph illustrating a spectral radiance of a black body foran object at 1875 K;

FIG. 3 is a chart illustrating a spectral emittance (emissivity) of avariety of stainless steel compositions having different compositions,temperatures, surface finishes, and degrees of oxidation;

FIG. 4(a) is an image of an exemplary embodiment of a melt pool thatillustrates a temperature distribution across the melt pool;

FIG. 4(b) is an image of the melt pool of FIG. 4(a) that illustrates anemissivity distribution across the melt pool;

FIG. 4(c) is an image of the melt pool of FIG. 4(a) that illustrates ameasured temperature without temperature-emissivity (TE) separationdistribution across the melt pool;

FIG. 4(d) is an image of the melt pool of FIG. 4(a) that illustrates anerror in measurement with respect to temperature distribution across themelt pool;

FIG. 5 is a schematic illustration of qualitative effects of thermalgradients and cooling rates on a microstructure of metals whenperforming additive manufacturing, such as when performing additivemanufacturing with the system of FIG. 1 ;

FIG. 6 is a schematic illustration of residual stresses generated from asingle selective laser melting (SLM) melt track;

FIG. 7A is a schematic illustration of a top view of normal thermalgradients across a surface of an object being additively manufactured;

FIG. 7B is a schematic illustration of a top view of abnormal gradientsresulting from porosity across the surface of the object of FIG. 7A;

FIG. 7C is a schematic illustration of a top view of the object of FIG.7A, illustrating a porous region identified from the abnormal gradientsof FIG. 7B;

FIG. 7D is a schematic illustration of a perspective view of the objectof FIG. 7A, illustrating a location and severity of porosity of theobject, which can be provided, for example, in an inspection report; and

FIG. 8 is a plurality of graphs illustrating spectral emissivities ofmaterials prepared via brushing, sandblasting, and wire electricaldischarge machining (EDM) at several temperatures.

GENERAL DESCRIPTION

Certain exemplary embodiments will now be described to provide anoverall understanding of the principles of the structure, function,manufacture, and use of the devices and methods disclosed herein. One ormore examples of these embodiments are illustrated in the accompanyingdrawings. Those skilled in the art will understand that the devices andmethods specifically described herein and illustrated in theaccompanying drawings are non-limiting exemplary embodiments and thatthe scope of the present disclosure is defined solely by the claims. Thefeatures illustrated or described in connection with one exemplaryembodiment may be combined with the features of other embodiments. Suchmodifications and variations are intended to be included within thescope of the present disclosure. By way of non-limiting example, termssuch as “manufacturing” and “printing” may be used interchangeablyherein and a person skilled in the art will understand the same. Stillfurther, the present disclosure illustrates some illustrations anddescriptions that includes prototypes, bench models, and or schematicillustrations of a set-up. A person skilled in the art will recognizehow to rely upon the present disclosure to integrate the techniques,systems, devices, and methods provided for into a product, such as aconsumer-ready, factory-ready, or lab-ready three-dimensional printer.

The present disclosure is generally directed to improved additivemanufacturing systems, devices, and methods that allow for real-timedata gathering and assessment of various parameters associated with theprinting. Various parameters and other data can be monitored andrecorded and, in some instances, those parameters and/or other data canbe utilized to adjust a print plan for the additive manufacturing.Alternatively, or additionally, the recorded parameters and data can berecorded but not actively used, at least not in approximate real-time.The parameters and data can be used for various assessments and otherinformation at a later point in time. The parameters and other data thatis measured, determined, or otherwise acquired can be obtained by way ofa spectrometer that is operated during the manufacturing process. Insome instances, the parameters and other data can be relied upon todetermine if any changes are needed to the way printing is occurring(i.e., the build plan). If changes turn out to be desirable, the systemis capable of implementing such changes during the manufacturingprocess. For example, if a defect is identified by the spectrometerdata, the system can be operated to reheat one or more layers that werealready printed to alter the printed component, removing the defect. Thepresent disclosure provides for other types of changes that can be made,and further, a person skilled in the art, in view of the presentdisclosure, will understand many different parameters that can bedetermined and/or acquired, as well as many different actions that canbe taken to allow the system to alter the build plan in real-time.

FIG. 1 provides one exemplary embodiment of a system 10 that enablesprecision process and quality control (PC and QC), with the systemincluding a three-dimensional (3D) printer 20, an imaging spectrometer40, and a real-time and/or post-processing apparatus or device 60, suchas an information processing device, processor, or controller.

The 3D printer can be many different types of printers known to thoseskilled in the art, including most any type of additive manufacturing(AM) printer. In the illustrated embodiment, the printer 20 is aselective laser melting (SLM) printer that includes a galvanometer 22, alaser 24, a lens 26, a first communication line 28, a secondcommunication line 30, and a melt pool 32. The illustrated embodimentalso provides for a housing 21 in which at least some of the componentscan be provided. In some embodiments, the melt pool, the powderedmaterial, and the component can be part of the housing 21 as shown. Forexample, the component, and material fused thereto at the melt pool, canbe effectively welded to the rest of the printer 20 via a build plate. Aperson skilled in the art will understand various configurations of 3Dprinters that are used in conjunction with SLM printing, and thus animage and detailed description of the same is unnecessary. Theillustrated embodiment is merely to provide a schematic illustration ofhow a spectrometer 40 can be used in conjunction with such a printer 20and other components. Likewise, a person skilled in the art willunderstand how an SLM printer works, and so the description of the sameis provided only in brief below.

As shown, the galvanometer 22 is in communication with a computationaldevice 80 (e.g., a computer, smart phone, etc.) by way of the firstcommunication line 28, and the laser 24 is in communication with thecomputational device 80 by way of the second communication line 30. Thecommunication lines 28, 30 allow the computational device 80 tocommunicate with the respective galvanometer 22 and the laser 24. Forexample, the galvanometer 22 can respond to an electrical current, andcan thus convert this electrical signal to rotary motion. Thecomputational device 80 can then scan or direct the laser toolpath byway of turning one or more mirrors 34 attached to the galvanometer rotoraxes. The laser 24 can be configured to direct a laser beam towards amirror 34 associated with the galvanometer 22 for eventual use at theprinting site, i.e., at the melt pool 32. As shown, a laser beam 25 canbe provided by the laser 24, deflected by the mirror 34, pass throughthe lens 26 to better focus the laser beam 25 at the intended target,and directed at the melt pool 32. In response, the melt pool 32 may meltto allow the material of the melt pool 32 to be formed into the desiredthree-dimensional component 36 being manufactured. The feedstock to befused to the component may be delivered as a powder or powdered material38, as shown towards the left, top portion of the melt pool 32 of FIG. 1. A person skilled in the art will appreciate that while thecommunication lines 28, 30 are shown as providing a hard-wire betweenthe computational device 80 and each of the galvanometer 22 and thelaser 24, in other embodiments such communication be achieved wirelessand/or by other mechanisms and means known to those skilled in the artfor transmitting data, signals, information, etc. (e.g., optical).Further, although the present illustration shows a single laser 24 andsingle laser beam 25, a plurality of lasers and/or laser beams ispossible.

The imaging spectrometer 40 also communicates with the computationaldevice 80 by way of a communication line 42. Similar to thecommunication lines 28, 30, communication between the imagingspectrometer 40 and the computational device 80 can be achieved in othermanners known to those skilled in the art, including but not limited towireless transmission. Transmissions between the imaging spectrometer 40and the computational device 80 can typically be data and otherinformation sensed or otherwise acquired by the spectrometer 40. Thespectrometer 40 can produce a hypercube 44, which provides data as afunction of both spatial position in the scene (X and Y) and a thirddimension that resolves wavelength (A). Each band 45 illustrated in thehypercube can be a different color to demonstrate temperatures (e.g.,red, orange, yellow, green, blue, violet). These data may be processedto extract other data products, such as a temperature profile 46, whichmay be used for direction control of the AM process, as described ingreater detail below. The illustrated temperature profile can likewisehave different color bands 47, such as yellow most central, then orange,and then red, although other colors and combinations are possible. Theillustrated hypercube 44 and temperature profile 46 demonstrate datathat may result from measurements made by the spectrometer, and aremerely provided for demonstrative purposes. Thus, no discussion of theillustrated results is needed, particularly because a person skilled inthe art will understand how to read and extract the desired informationfrom the hypercube and temperature profile. Further, discussions of themany types of parameters the spectrometer 40 can measure, and how thoseparameters can be stored and/or utilized, are provided for below.Parameters that can be measured by the spectrometer include but are notlimited to one or more of a temperature distribution, emissivity, bandratios, radiation transport characteristics, and a melt pool shape.Additional details about such parameters are provided herein, or areotherwise understood by a person skilled in the art in view of thepresent disclosures. In some instances, the parameters can be sufficientto allow the controller 60 to detect one or more defects in theplurality of fused or deposited layers, and the controller 60 can beconfigured to adjust the build plan such that at least one defect of theone or more defects is remediated by the controller 60 adjusting thebuild plan to allow for a portion of the plurality of fused or depositedlayers to remelt material included as part of the portion of theplurality of fused or deposited layers. The controller 60, or some othercomponent of the system 10, can be configured to perform at least one ofthe following as part of remediation: use one or more recovered controlsignals to quantify defect morphology; deliver extra feedstock to thedefect location; calculate optimal scan parameters in view of defect andfeedstock properties; or apply one or more scan parameters to the buildplan.

Parameters and other data measured or otherwise acquired by thespectrometer 40 can be recorded in a storage unit (e.g., computer,database, cloud, etc.) using any number of techniques known to thoseskilled in the art for recording, such as storing it in a computer harddrive, a database, in a cloud setting, incorporated into an inspectionreport, and/or used to generate a digital twin model.

The post-processing apparatus or device 60 is merely illustrated as abox in communication with the computational device 80 via acommunication line 62 because a person skilled in the art will recognizemay different types of post-processing apparatuses and devices that canbe used in conjunction with the present disclosure. Like the othercommunication lines provided for herein, the communication line 62 ismerely representative of communication that can occur between thepost-processing apparatus 60 and the computational device 80. Othermeans of communication (e.g., wireless, optical, etc.) can be reliedupon. A person skilled in the art, in view of the present disclosures,will be able to understand the types, configurations, and set-ups forany real-time and/or post-processing apparatuses that are used inconjunction with the provided for systems, devices, and methods. Someexemplary real-time and/or post-processing apparatuses are informationprocessing devices, including processors or controllers, that are ableto make adjustments to the build in response to information gathered bythe spectrometer. The functionality and capabilities of such processorsand controllers is provided in greater detail below (e.g., processcontrol microstructure control, and many others). Other post-processingapparatuses or devices can include those that can actually modify thecomponent 36 being manufactured. Some other non-limiting examples ofpost-processing apparatuses or devices that can be used in conjunctionwith the present disclosures include: hot isostatic pressing, tomitigate measured porosity; targeted heat treatment, to mitigatemeasured excessive residual stress; and/or post machining, to mitigatemeasured geometric (form) errors.

The controller 60 can be configured to determine statistical moments ofthe of the parameters measured by the spectrometer 40. Alternatively, oradditionally, the controller 60 can also be configured to extract atleast one of spatial derivatives or temporal derivatives and processsuch derivative(s) to generate a quality control assessment and/or aprocess control signal. Such assessments and signals are discussed ingreater detail below. The statistical moments can include averagesand/or variance, with the averages and/or variance being along one ormore of a spatial, temporal, or spectral dimensions of recorded data.Statistical moments can include one or more of a spatial moment, atemporal moment, and a spectral moment. The controller 60 can beconfigured to perform temperature-emissivity separation on the measuredparameter(s) (e.g., a first spectrally resolved radiance measurement anda second spectrally resolved radiance measurement) by using a twotemperature method. Alternatively, the controller 60 can be configuredto perform temperature-emissivity separation on the measured parameters(e.g., by assuming constant emissivity across a single pair of spectralbands that include a spectrally resolved radiance measurement) by usinga grey body method. Both methods are described in greater detail herein.

A variety of other capabilities of the controller 60 are possible. Forexample, the controller 60 can be configured to qualify the dimensionsof the component 36 being printed in view of emissivity data that ismeasured by the spectrometer 40. By way of further example, thecontroller 60 can be configured to assess parameters of the melt pool 32in view of anomalous spectral features measured by the spectrometer 40.

Although the present disclosure primarily discusses one form of AMprinting, SLM printing, a person skilled in the art will understand howthe disclosures provided for herein can be applied to other forms of 3Dprinting as well, thus allowing for PC and QC during other types of 3Dprinting (e.g., electron beam additive manufacturing (EBM), selectivelaser sintering (SLS), selective laser melting, fused depositionmodeling (FDM), fused filament fabrication, other forms of depositing orfusing by melting, use of chemical reactions, etc.). Further, any numberof imaging spectrometers that can be used in conjunction with thepresent disclosures, including but not limited to spectrometers. To theextent any adaptations or modifications for such spectrometers aredesired, a person skilled in the art, in view of the presentdisclosures, will be able to make such adaptations and modifications.Typically, such adaptations and modifications are not necessary. Theoperating principles, data processing, and AM work flows integral to thesystem of FIG. 1 , and other provided for systems, devices, and methods,are detailed below.

Adjustments to build plans are described below. In some instances, thecontroller 60 can effect those adjustments. Alternatively, thecomputational device 80 can do so. Some non-limiting examples of suchadjustments include: adjusting an energy profile of a first laser of theAM printer 20 to maintain a desired temperature gradient about a portionof one or more lasers of the plurality of lasers that is melted by asecond laser of the AM printer; a power and/or a scan pathway of a laserof the AM printer 20 to provide a substantially constant temperaturealong a desired region of the melt pool 32 as a beam of the AM printerscans; adjusting an energy profile of one or more process lasers 24 ofthe AM printer 20 using at least one of adaptive feed-forward control orfeedback control; adjusting the energy profile of the one or moreprocess lasers 24 of the AM printer 20 using previously recorded processdata; and/or adjusting the energy profile of the one or more processlasers 24 of the AM printer 20 in conjunction with at least one of a CADcode or an analysis of corresponding machine code to generate controlsignals. The controller can be configured to terminate manufacture ofthe component 36 prior to completion of the build plan in response to adefect detected by way of one or more of the parameters measured by thespectrometer 40. Non-limiting examples of energy profiles can includeenergy delivered as a function of location within the laser spot, whichmay incorporate Gaussian, quasi-Gaussian, top-hat, or donut character.

Signals of Interest

A great number of radiation transport (RT) phenomena are capable ofgenerating data with a unique spectral signature as a result of a 3Dprinting process. The application of multispectral sensing to theblackbody radiation emitted by an object is detailed below. However,spatially and spectrally resolved data may extract actionable controland defect detection signals from at least the following processes:

-   -   Spectrally resolved reflectance or transmission of the build        material, including when illuminated with an external source;    -   Raman spectroscopy, which may prove useful in assessment of        de-alloying or contamination;    -   Selective emissions (i.e., emissions occurring at a sharp        spectral peak), especially in SLM and EBM processes;    -   Band ratios of selective emissions;    -   Absorption measurements, specifically of the plume generated by        the beam-material interaction in SLM or EBM processes; and    -   Combinations of the above.        A person skilled in the art will understand other processes from        which spatially and spectrally resolved data may extract        actionable control and defect detection signals in view of the        present disclosures.

Blackbody Radiation

All objects emit radiation simply as a result of having a non-zerothermodynamic temperature. Light emitted in this manner is known asblackbody radiation. Equation 1a, provided below, yields the radiance ofa blackbody (By) as a function of wavelength (A) and temperature (T) byway of physical constants. This function is plotted for an object at1875 K in FIG. 2 . As shown, that radiance is typically a strongfunction of wavelength. This equation is invertible, meaning if Ba isknown for some range of A, T can be uniquely determined.

$\begin{matrix}{{B_{\lambda}\left( {\lambda,T} \right)} = {\frac{2{hc}^{2}}{\lambda^{5}}\frac{1}{e^{\frac{hc}{\lambda k_{b}T} - 1}}}} & {{Equation}\text{.1}a}\end{matrix}$

Materials generally deviate from this idealization. Equation 1b,provided below, includes a multiplicative factor E (λ) to describe theefficiency with which a surface undergoes thermal emission or absorption(emissivity of the material). This correction essentially describes theefficiency by which a surface can emit (or by reciprocity, absorb)radiation of a specific wavelength. For certain materials, E (λ) may beapproximately constant over some range of λ. These classes of materialsare often called grey bodies, and many materials, especially polymers,display this behavior over limited wavelengths.

L _(λ)(λ,T)=E(λ)B _(λ)(λ,T)  Equation. 1b

Two color pyrometry is an effective method for measuring such materialsover limited temperature ranges. In this process, radiance issimultaneously measured in two bands. The emissivity is assumed to beconstant over both bands (colors), thus one may determine this value andestimate the temperature of the object. For grey body materials thisprovides a reasonable assessment of temperature. However, this methodstruggles when applied across wide temperature ranges, at least becausesensing is only optimal when the colors lie along the steep portions ofthe blackbody curve, and is further compromised when emissivitysubstantially varies with wavelength. Alternatively, approaches thatrely on wide-band image data typically do so in conjunction with anassumed emissivity value to assess temperature of an AM surface or meltpool (described further below). Approaches of this nature, like otherexisting, deficient approaches, do not rigorously account for thetime-varying and spatially-varying optical properties of a buildmaterial undergoing processing, and thus are not as effective as thepresent disclosures.

Most metallic materials deviate greatly even from greybody behavior.Emissivity of metals typically is a strong function of wavelength,(thermodynamic) phase, surface finish, temperature, and surfacecontamination. There is also variability even for similar alloys, as iscaptured for stainless steel alloys in FIG. 3 . As shown, at least 23different stainless steel specimens are charted to demonstrate thecorrelation between their wavelength and the normal spectral emittance.Variables such as the temperature, surface finish, and degree ofoxidation of the specific materials used in the various alloys can havea significant impact on spectral emissivity. However, even if E (λ) is anon-constant function, it may still be possible to invert Equation 1afor a radiometric temperature if this dependence is known.

In practice, however, it is nearly impossible to accurately predict theradiative properties of build material in 3D printing processes. In SLM,for example, some powdered build materials initially display near idealblackbody behavior over spatial length scales that are long as comparedto powder particle dimensions. The powder then undergoes a phase changeas it melts, thereby becoming mirror-like with low emissivity. The meltpool then solidifies, leaving a rough surface texture of unknown andinexact surface chemistry and geometry, imparting yet another radiativecharacter somewhere between that of the powder and liquid. Other effectspresent when metals undergo solidification can include segregation,which can cause the radiative properties to change, rapidly, as thetemperature of the location of interest changes. Because of this greatvariance in radiative behavior and unknown distribution thereof,accurate temperature retrieval cannot make use of a-priori knowledge.

Instruments used in the scientific literature and available commercially(see Table 1) either use metrics that do not need to resolve temperature(e.g., simply looking for radiance fluctuations as an indicator ofprocess stability), or make inexact assumptions about the material underinterrogation to make purely relative measurements. One common approachis to either use grey body approximation and use an arbitrarily selectedemissivity regardless of the condition of the material (e.g., powder,liquid, solid), or ignore the effect entirely and assume blackbodybehavior. The results from applying blackbody behavior to typical SLMtemperature and emissivity distributions are shown in FIGS. 4(a)-4(d).More particularly, FIGS. 4(a)-4(d) provide for a simulation of an SLMmelt pool which illustrates the need for temperature-emissivity (TE)separation to extract accurate radiometric temperature. Specifically,FIGS. 4(a) and 4(b) illustrate temperature and emissivity distributions,respectively, typical of an SLM melt pool. If the emissivity is assumedto be unity, i.e., blackbody behavior, the estimated (measured)temperature distribution is shown in FIG. 4(c). The error in thismeasurement (essentially values in FIG. 4(a) minus values in FIG. 4(c))is plotted in FIG. 4(d). The error in temperature estimation is shown tovary with position and approach inaccuracy of up to about 1000 K. Thus,as is clearly evident, ignoring emissivity makes accurate determinationof temperature practically impossible via radiometric means.

TABLE 1 Commercially available AM PC/QC modules. Adapted from [6].Process Manufacturer Module Name Target Phenomenon Process VariableApparatus EBM Arcam LayerQam Porosity N/A Camera SLM Sigma Inc.PrintRite 3D Unknown N/A Thermocouple and Camera Concept Laser QM MeltPool Melt Pool Monitoring Laser Power High Speed Camera EOS N/A UnknownN/A Camera DED DEMCON LCC100 Melt Pool Monitoring Laser Power CameraDM3D Technology DMD Closed Loop Melt Pool Monitoring Laser Power 2 ColorPyrometer Feedback System and Build Height and 3 High Speed CamerasLaser Depth LD-600 Depth Measurement Laser Power Inline Coherent ImagingPromotec PD 2000 Melt Pool Monitoring N/A Camera PM 7000 Melt PoolMonitoring N/A 1D Photodetector Stratonics ThermaViz Melt PoolTemperature Laser Power Two Color Imaging Pyrometer

Temperature-Emissivity Separation

Temperature-Emissivity (TE) separation, or the process of decoupling theeffects of variable spectral emissivity from the temperature of anobject given radiometric data, is a difficult process. For example,consider a single thermal radiance measurement comprised of n uniquespectral bands on a material of unknown spectral emissivity. It isimpossible to invert these measurements for a temperature as the systemwill always consist of n+1 unknowns: the emissivity of the n bands andthe actual temperature T of the object. Thus, neither multispectral orhyperspectral data directly provide a solution to this problem. Rather,the improvements lie in having more spectral bands (colors), therebyensuring that some portion of the blackbody curve with contrast isalways captured, and the ability to perform TE separation using weakassumptions as discussed below.

Performing TE separation on multispectral data relies on breaking theabove relations between the number of measurements and unknowns. Twonon-limiting examples of TE methods that are suited to the absence ofa-priori knowledge and inherent process dynamics inherent to common 3Dprinting processes include: the two temperature method (TTM) and thegrey body method (GBM).

The TTM method relies on making two measurements of the scene atdifferent temperatures, and inverting the resulting 2n radiancemeasurements to recover n emissivities and two temperatures. A leastsquares method may be used to solve this overdetermined system. Thistechnique is predicated on emissivity remaining constant betweentemporally adjacent measurements. For data recorded at high speeds, thismay be a reasonable assumption, and the large temperature fluctuationsinvolved in many AM processes make this an effective technique.

A different approximation is made in the GBM. Here, the emissivity isassumed to be an arbitrary but identical value for a single pair ofspectral bands, meaning the n measurements are used to recover n−1emissivities and one temperature. This approximation does not rely onthe time correlation necessary with the TTM, and is a particularly goodfit if spectral data are finely sampled (small spacing in wavelengthbetween adjacent spectral bins).

With either of these two methods, it is possible to extract an accurateradiometric temperature without knowledge of the exact composition orradiative properties of the subject. As described below, thisinformation may be used to inform AM processes to create parts withsuperior properties and improved quality assurance.

Process Control

Accurate temperature measurements make high fidelity, in-situ processcontrol possible. Many existing systems rely on either point-thermalmeasurements that provide an average measurement across the entire meltpool, or rely on wide-band image data to extract approximate melt pooldimensions. The provided for process control techniques described belowimprove upon existing process control techniques and enable novelprocessing capabilities.

With the system described herein, laser power, scan speed, laser path,and spot size, among other parameters, may be adjusted in response to anaccurately measured temperature spatial profile. Moreover, melt poolshape may be more clearly defined by examining the spectral emissivityfor the sharp change in radiative properties between molten andsolidified material.

Microstructure Control

High fidelity, high speed measurements of temperature gradients alsoenable one to control the rate of heating and cooling of build material.As is shown in FIG. 5 , which provides for qualitative effects ofthermal gradients and cooling rate (labeled as solidification frontvelocity) on the microstructure of metals, high solidification frontvelocities or low thermal gradients results in an equiaxed grainstructure. Conversely, large thermal gradients or slow solidificationfront velocity results grains that grow in fractal-like structurescalled dendrites. These ultimately form elongated columnar grains, whichgenerally grow along the direction of greatest thermal gradient.Increasing the rate of heat flux (in the region about the line denotedhigh {dot over (T)}) can shrink the characteristic dimensions of thepredominate structure, and decreasing the rate of heat flux (in theregion about the line denoted low {dot over (T)}) can have the oppositeeffect. Finally, extremely low solidification front velocities canresult in single crystal (plane front solidification) or comparativelyfew, very large crystals (cells). Knowledge of how the scan parameterscan influence the temperature distribution and its rate of change canenable alteration of scan parameters to achieve a desired microstructuresuch as those described above. This microstructure may be uniform acrossan entire component, or functionally graded (altered as a function ofposition) to meet the requirements of a high performance application.

Residual Stress Management

One of the central problems with temperature-based AM processes is theresidual stresses that are generated due to the inherently large,non-uniform thermal gradients encountered. The gradients that occur as aresult from a single SLM melt track are illustrated in FIGS. 6A and 6B.Specifically, FIG. 6A illustrates the response of a component 600 toheating via exposure to laser radiation 610 in cross section. Thislocally heats the region 620, resulting in a thermally induced strain(denoted ε_(th)), or thermal expansion. Upon removing the heat source610, this material solidifies and locally attempts to contract, as shownin FIG. 6B. However, this relaxation is prevented by the rest of thecomponent, meaning the thermally induced strain ε_(th) is essentiallyfrozen into the heated region 620 of the component. This manifests asthe residual stresses σ_(tens), a tensile stress in the heated region620 from this strain, and a reaction stress σ_(comp) in the rest of thecomponent 600 that is substantially compressive in character. However,the interaction of many melt tracks and effects of reheating leads tofar more complex stress states. Accurate thermal measurements may enablereduction of these thermal gradients, or printing in a fashion thatinduces favorable residual stresses for a specific application.Compressive residual stresses at the exterior of a component may begenerated through preferential preheating of the interior region tomitigate cracking. The interior can be placed in a state of tensilestress as it undergoes greater thermal shrinkage when cooled. Atequilibrium, these stresses are countered by compressive stresses at thecomponent's exterior. By way of non-limiting example, such a work flowcan include the following steps:

-   -   Print layer;    -   Measure the temperature distribution of the part;    -   Optionally reheat the part in specific locations if the        temperature distribution is not favorable;    -   Generate toolpaths to scan the next layer to achieve the desired        residual stress profile;    -   Recoat the build platform; and    -   Repeat until component is complete.

Determination of the optimal thermal profile may be heuristic, informedby computer modeling, and/or experimentally determined. High resolutionoptical measurement of component boundaries and their movement undercooling comprises a mechanism by which the resulting residual stressesmay be verified nondestructively and in-situ.

In view of the present disclosures, the work flow can be altered basedon the various parameters measured by the spectrometer, which in turncan cause changes in the build plan for the component being built. Whenchanges occur to the build plan, they will then also impact the workflow. Accordingly, a work flow may also include steps such as measureparameters, store those parameters, and/or adjust the build plan in viewof one or more of the measured parameters. The result can be modifyingportions of the component that were already printed to fix errors,remediate defects, etc. and/or to add aspects to the component, such asto provide additional support where it is identified to be needed.Aspects of the build plan that can be modified or adjusted include ascan speed, a laser power, a laser scan path, a spot size, and/or a rateof heating or cooling a material being fused or deposited in one or morelayers of the plurality of layers of the component.

The build plan can be adjusted in a variety of ways. Some non-limitingexamples include: altering one or more laser scan parameters of the AMprinter to achieve at least one of a desirable thermal profile, thermalgradient, or a time derivative temperature within a defined region ofthe component being printed; selectively re-heating at least a portionof at least one layer of the plurality of layers to achieve at least oneof a desirable thermal profile, thermal gradient, or a time derivativeof temperature within a defined region of the component being printed;and/or adjusting a temperature of at least one of a melt pool of the AMprinter or at least a portion of at least one layer of the plurality oflayers to alter at least one of a microstructure, residual stress, orsurface roughness of a defined region of the component being printed. Adefined region can be viewed as any region that is definable by thesystem or a user thereof, such as a proximal scan vectors, tiles,regions, etc. Achieving a desirable thermal profile can be as simple asheating a portion of the layer to a target temperature, i.e., meltingit. A person skilled in the art, in view of the present disclosures,will understand various ways a desirable thermal profile can beachieved. Melting it is merely one option. An optimal profile may or maynot need to be so hot as to melt it. Other non-limiting ways by which abuild plan can be altered include: (1) measuring during the nominalbuild process, e.g., information from one layer is used to determine acorrective action, before or during the subsequent layer(s); (2)measuring before the nominal build process, e.g., running a test patternin the corner of the build area to determine a transformation necessaryto the build parameters, and/or a test pattern across the build area tocalibrate out machine errors; and/or (3) measuring before the layer isfused or deposited, for instance there could be a pre-layer measurementroutine, e.g., inspecting the unfused powder layer for its thermalproperties or local anomalies, and correcting the layer-wise build planaccordingly.

Porosity Control

While porosity is typically considered to be a defect, a person skilledin the art may envision a functionally graded component in whichporosity is intentionally used to achieve gradients in componentdensity. The systems, devices, and methods described herein are capableof quantitatively assessing porosity over distinct regions, therebymaking controlled induction of porosity possible. Methods for inducingporosity include thermally activated gassing agents, dissolution of gasfrom the melt pool, and keyholing, all of which can benefit from, andarguably demand, precision temperature control. One simple way to affectporosity control can be to tune the laser scan parameters so porosityremains due to incomplete fusion of the powder (at low energy density)or can be generated by application of an excessive energy density,relative to the energy density that causes complete fusion. Moreover,the surface texture, and therefore emissivity, changes rapidly as thematerial foams (i.e., subsurface pores form due to trapped gas), whichcan cause difficulties in temperature extraction of the nature notedabove.

Feed-Forward Adaptive Control of SLM

Attempts to improve SLM have centered on changing laser power in directresponse to melt pool radiance. The present disclosure, on the otherhand, provides for improved systems, devices, and methods that not onlyinclude feedback control, but also provide for leveraging adaptivefeed-forward control to reduce variance in process parameters. Suchsystems, devices, and methods can use time-temperature profiles (orother spectral data) from nearby scan vectors in the current or previouslayers. Consider, for example, the process of printing the first layerof an overhanging feature of a part. As the first scan vector sweepsfrom printing above previously solidified material to printing abovepowder, the cooling rate of the melt pool greatly decreases due to themore resistive thermal pathway surrounding the melt pool. Based onmeasurement of the local temperature gradient, the provided for adaptivecontroller can switch from parameters optimized for printing in ahigh-thermal-diffusivity region to those optimized for printingoverhanging features. Such adjustment may be performed on a continuousbasis throughout the part. The next (parallel and adjacent) scan vectorcan then be flagged to expect the same change in conditions atapproximately the same location along the vector. Likewise, the secondlayer above the overhang can feature thermal diffusivity somewherebetween printing over fused material and over powder. This region isknown a-priori from the data of the first layer, again enablinglayer-specific or area-specific scan parameters to be computed andfed-forward.

In-Situ Calibration of SLM Scan Parameters

The SLM process may be directly calibrated using a pattern of known,systematically varied laser parameters used to irradiate a designatedsub-region of the build platform, prior to fusing the desired component,or during the build process (e.g., when recalibration is desired).Optical interrogation, as described herein, can be used to assess theresulting quality of the scanned regions. The optimal set of scanparameters can then be selected to print the component or part. Thispattern may be run once at the beginning of a part, as a continuouswitness structure printed every layer, or every few layers as printconditions drift. For instance, the relationships between the scanparameters in the test pattern and the temperature distribution on thebuild surface, melt pool geometry, etc., can be assessed and used toadjust the scan parameters and/or the scan pattern for the remainder ofthe build.

Performing this calibration in-situ has a great number of advantagesover simple application of a pre-defined recipe. While the latter may besufficient to select scan parameters under well-controlled conditions(e.g., known layer height and material), not all relevant parameters aregenerally known to the user of an SLM machine. For example, powdermorphology can greatly affect the fusion process, and has been observedto evolve unpredictably as unfused material is recycled and mixed withfresh powder. Performing the calibration as an in-process step allowsfor optimization of scan parameters in a manner that rigorously accountsfor the inaccuracies and unknowns of the machine and material used.Further, the results of this metrology may be used to adjust a nominalprocess parameter set that is determined by build planning software, andthe results of the metrology may be used to update settings in the buildplanning software.

Compensation for Recoating Faults and Powder Variation

In some embodiments, the recoating process between subsequent layers mayresult in a substantially perfect uniform layer of powder, from which alayer can be fabricated. However, machine miscalibration ormisalignment, changing boundary conditions arising from the nature ofthe previously fused layer, and/or the stochastic nature of powderparticle movement may all manifest as variations in the characteristicsof the powder layer across the build platform. This variance can causedefects in complete components, such as porosity resulting fromunder-fusion in regions of excessive powder deposition.

The present disclosure provides means for quantification and mitigationof such defects, through the ability to discriminate materials basedupon their radiative signatures. Here, a multi-spectral image of thebuild platform can be taken after recoating, optionally after heating toa temperature below that required to fuse the material to enhanceblackbody emission. The measured emissivity of each pixel can then becompared to previously measured spectral emissivity functionscorresponding to bare, fused material and a deep layer of powderedmaterial. Such a comparison may be performed, for example, by taking thenormalized inner product to quantify the degree to which the regionsubtended by a pixel corresponds to one of these conditions. Regionslacking in powder density can exhibit more radiative character from theprior layer, whereas the radiative character of the powder can dominatewhere excess powder has been deposited.

These data may be used to alter process parameters, including laserpower, spot size, and spot path, such that fluctuations in materialproperties of the completed article can be minimized. At the most basiclevel, laser power can be adjusted in a manner corresponding to powderdensity such that the material is fully melted, while simultaneouslypreventing excessive vaporization. If large recoating defects are found,the printer can be signaled to re-recoat the layer to improveuniformity.

Quality Control

Existing methods for quality assessment of AM components have centeredon detecting temperature anomalies during the build. Mean melt pooltemperature excursions can be correlated with component porosity asassessed via computed tomography (CT). However, the point temperaturemeasurements often provide insufficient data, both in terms of accuracyand inability to resolve spatial variations. Thus, prior to the presentdisclosures, this information did not provide a quantitative metric.There are some methods for optical determination of part porosity thatuse a wide-band sensor to extract thermal diffusivity data. To theextent this information provides a quantitative metric, the methods aregenerally ineffective due to the limited accuracy of the thermalmeasurements. Moreover, the averaging techniques employed in suchmethods only provide a blanket assessment of the entire part, not alocalized mapping of component defects as provided for herein. Throughextracting superior thermal information, the present disclosures aim tonot only improve upon these works, but enable the described novelmethods for in-situ quality control.

Dimensional Accuracy

Because the systems, devices, and processes outlined herein extract theemissivity of the scene on a pixel-by-pixel basis, this information maybe used to accurately discriminate between powdered and solidifiedmaterial. In this case, dimensional accuracy of a feature may beoptically assessed by looking for the sharp change in emissivity frompowder to solid. Geometry fabricated via AM that is inaccessible toconventional machine tools generally implies that it is alsoinaccessible to conventional metrology (i.e., post-build inspection bycoordinate measuring machine). The ability to accurately inspect thesefeatures in-situ, before they become inaccessible by subsequent layers,provides an attractive alternative to post-build CT.

Thermal Diffusivity Tomography

Accurate temperature retrieval also enables novel QC methods, such asthermal diffusivity tomography. As illustrated in FIGS. 7A-7D, afterprinting a layer 510 of a component 500, the process laser(s) (or analternate means of heating) may be used to induce a thermal gradient inthe component 500 at one or more locations without influencing thecharacteristics of the part (e.g., without causing recrystallization orremelting). Two potential thermal gradients are illustrated in FIGS. 7Aand 7B, in which a laser spot 512 is shown on the layer 510, and thegradient is illustrated by various bands 514, which can be differentcolors based on the temperature gradient (e.g., yellow, then orange,then three shades of red in the provided embodiment). FIG. 7Aillustrates a substantially uniform thermal gradient consistent withthermal energy propagating through homogenous material comprising thecomponent.

Conversely, FIG. 7B illustrates a distorted thermal gradient, as theporosity serves to locally lower the thermal diffusivity of thecomponent. The resulting gradient can then be measured. In the interiorof the part, nonuniformities in the temperature gradient imply voids,cracking, porosity, and other defects. As shown in FIGS. 7B, 7C, and 7D,the component 500 can have a porous region 516. Because data can becollected layer-by-layer, as illustrated by FIG. 7C, an inspectionreport mapping the exact three-dimensional size and shape of printdefects can be computed, for example by stacking the 2D measurements, asbest illustrated in FIG. 7D. This process is distinct from typicalimplementations of thermal diffusivity tomography as a quality assurancemeasure, wherein experiments must be exclusively performed by heatingand observing the exterior of a component.

Defect Remediation

The processes for defect identification described herein providequantitative methods to detect defects, either in departure from optimalprocess conditions or by post-layer inspection. This knowledge of defecttype and severity is significant for effective remediation before addingsubsequent layers to the part. For example, attempting to correctporosity in an SLM part can easily make matters worse if the energyapplied vaporizes more build material than volume void removed. Armedwith this information, one is able to deterministically repair minordefects in components before they become inaccessible by furthermaterial deposition. In one non-limiting example, this process caninclude the following steps:

-   -   Perform in-process quality control;    -   Assess severity of defects;    -   Generate toolpaths and process parameters to remediate (e.g.,        remelt) the area around the defect, either before or after        recoating;    -   Perform remediation; and    -   Perform in-process quality control to verify defect removal.

The present disclosure provides for many ways by which a build plan canbe modified. In some instances, the spectral data can be used toidentify voids and/or classify voids the can benefit from remediation.For example, information about pore size, location, and void fractioncan be utilized, in conjunction with other knowledge of those skilled inthe art (e.g., information about the material that is known), can beused to quantify morphology. This, in turn, can be used to modify thebuild plan. For example, the system can compute how much energy isneeded to deliver material to fill a void without provided excess energythat could cause problems, such as keyholing.

Melt Pool Contamination

Spectral data also provide means for assessment of melt poolcontamination. One of the major drivers of the variability illustratedin FIG. 3 is the presence of surface oxidation. Moreover, selectiveemission and/or absorption features also leave a spectral fingerprint bywhich specific contaminants such as oxides or water vapor may beidentified. The present disclosures allow for a melt pool to be analyzedby the spectrometer to determine if any contamination has occurred,thereby identifying the issue prior to completion of a build.

Surface Finish

Despite greatly complicating temperature retrieval, a spectralemissivity of a component's surface is useful as a PC/QC controlparameter in its own right. Abnormalities in surface finish can beindicative of an unstable manufacturing process. Moreover, the abilityto measure surface finish provides the means to laser polish favorablyoriented surfaces to a controlled level. As an example measurement, FIG.8 shows clear differences in the spectral emissivity of specimensprepared with a variety of surface finishes. More particularly, threedifferent types of material (Inconel 718, Rene 41, and Haynes 25) weretreated in three different manners (brushed, sandblasted, and wire-cutEDM), and then tested. The treatments in particular demonstratesubstantial effects, with sandblasting raising the spectral emissivityof each of the three materials as compared to brushing, and wire-cut EDMproducing even higher spectral emissivities than sandblasting. While notstrictly representative of surface finishes produced via additiveprocesses (e.g., SLM), these measurements show that surface finishcharacteristics greatly alter emissivity, or conversely, that emissivityprovides a means for interrogating the unknown surface finish of acompleted component.

Control of Multi-Laser SLM Apparatus

The introduction of a second process laser (or further additional lasersor beam shapes) can enable higher fidelity thermal management. A personskilled in the art will understand that the impetus for multiple lasermachines can be an enlarged build area. Typical implementation is suchthat the overlap of the scan area between lasers is minimized, and eachprocess laser controls a melt pool completely independent of the other.Conversely, in many instances, the present approach relies on having theoverlapping scan area large enough to fully contain the part ofinterest. This allows for both lasers to work in a coordinated manner torealize a desired spatial and/or temporal thermal profile of the buildregion.

A non-limiting example configuration uses one laser to provide energyfor preheating a region about the melt pool. In such an instances, thislaser can bring the material near, without exceeding, its melting point.The second laser then only needs to supply enough energy to melt thematerial from this elevated temperature, thus reducing the spatialthermal gradient about the melted region.

If the aggregate actions of both lasers are considered, it becomesevident that the laser power and/or energy distribution may need to bealtered as a function of time. Energy from the second (melting) lasercan diffuse outwards from the melt pool, thereby heating the surroundingmaterial. Thus, the pre/post heat laser intensity can be reduced inresponse to the thermal energy from the melting laser. Precision thermalmonitoring using an imaging spectrometer can enable these parameters tobe tuned in direct response to process conditions that may vary greatlywith material and build geometry. This approach may be extended tosystems with arbitrary numbers of laser spots, having shapes includingbut not limited to dots or lines, with synchronized or independentcontrol. Also, this may apply to reshaping the intensity profile of asingle beam or multiple beams, where each beam may be formed usingoptics that operate on one or more laser sources.

Comparison to Post-Build Computed Tomography

The primary alternative to precision process control and in-situ qualityassessment is post-build computed tomography (CT), which is arguably theindustry standard at present for void detection and dimensionalverification. CT scanning relies on X-ray radiation to differentiatematerials based upon their electron density. Due to the mechanism bywhich X-ray tubes operate, volumetric resolution is limited to about 5μm for samples below about 1000 mm³ and falls off proportionately forlarger samples. The present disclosure, because it provides anassessment of part quality during the build process, enables defectremediation or termination if an unrecoverable defect is found.Moreover, the present disclosure permits the fabrication and inspectionprocesses to run in parallel, as opposed to requiring a post buildinspection requiring many CT machine-hours.

Commercial Applications

The tremendous growth of AM has been driven by the complexity-for-freedesign paradigm. The ability to fabricate otherwise impossiblegeometries extends the design space for engineering components, andoften allows for replacement of multi-part assemblies with monolithiccounterparts. Extending this trend to mission critical applicationstypically requires stringent process control to ensure the full strengthof a component is realized. Although there are existing commercialproducts that improve the performance and quality assurance of AMcomponents by observing the fabrication process, none have provenadequate to bring additive processes under complete, or near complete,control, nor replace conventional post-build inspection processes.

Demand for advanced PC/QC for additive processes is proven by the add-onmodules listed in Table 1 above. While the instrumentation describedherein integrates similarly to 3D printing equipment, its improvedfidelity and advanced data processing enable closed-loop control overnovel additive techniques and increased confidence that the fabricatedcomponent performs as specified.

One skilled in the art will appreciate further features and advantagesof the disclosure based on the above-described embodiments. Accordingly,the disclosure is not to be limited by what has been particularly shownand described, except as indicated by the appended claims. Allpublications and references cited herein are expressly incorporatedherein by reference in their entirety.

1. A system for manufacturing a three-dimensional object, comprising: anadditive manufacturing printer configured to fuse or deposit a pluralityof layers to manufacture a three-dimensional object according to a buildplan; an imaging spectrometer configured to measure one or moreparameters while the plurality of layers are fused or deposited by theadditive manufacturing printer and transmit one or more signals thatcorrelate to the one or more measured parameters; and a controllerconfigured to receive the signals that correlate to the one or moremeasured parameters, determine if any changes to the build plan aredesirable in view of the one or more measured parameters, and, ifchanges are determined to be desirable, adjust the build plan in view ofthe one or more measured parameters while the additive manufacturingprinter is still in the process of manufacturing the three-dimensionalobject.
 2. (canceled)
 3. (canceled)
 4. The system of claim 1, whereinthe one or more measured parameters includes at least one of atemperature distribution, emissivity, band ratios, radiation transportcharacteristics, or a melt pool shape, and wherein the controller isconfigured to determine statistical moments of the one or moreparameters, and wherein the controller is configured to extract at leastone of spatial derivatives, temporal derivatives, or spectralderivatives, and process the at least one of spatial derivatives ortemporal derivatives to generate at least one of a quality controlassessment or a process control signal. 5-10. (canceled)
 11. The systemof claim 1, wherein an adjustment to the build plan by the controllercomprises at least one of the following adjustments: altering one ormore laser scan parameters of the additive manufacturing printer toachieve at least one of a desirable thermal gradient or a timederivative temperature within a defined region of the three-dimensionalobject; selectively re-heating at least a portion of at least one layerof the plurality of layers to achieve at least one of a desirablethermal profile, thermal gradient, or a time derivative of temperaturewithin a defined region of the three-dimensional object; or adjusting atemperature of at least one of a melt pool of the additive manufacturingprinter or at least a portion of at least one layer of the plurality oflayers to alter at least one of a microstructure, residual stress, orsurface roughness of a defined region of the three-dimensional object.12-19. (canceled)
 20. A method for additive manufacturing, comprising:depositing or fusing one or more layers of a plurality of layers of athree-dimensional object, the depositing or fusing being performed by anadditive manufacturing printer; measuring one or more parametersassociated with the one or more layers using an imaging spectrometer,the measuring occurring prior to depositing or fusing all layers of theplurality of layers of the three-dimensional object; communicating theone or more measured parameters to a controller for processing thereof;processing the one or more measured parameters using the controller todetermine if any adjustments to the deposited or fused one or morelayers are desirable; and if the controller determines one or moreadjustments to the deposited or fused one or more layers is desirable,communicating commands to the additive manufacturing printer toinstitute the one or more adjustments to the one or more deposited orfused layers prior to depositing or fusing all layers of the pluralityof layers of the three-dimensional object.
 21. The method of claim 20,wherein communicating commands to the additive manufacturing printer toinstitute the one or more adjustments to the one or more deposited orfused layers prior to depositing or fusing all layers of the pluralityof layers of the three-dimensional object comprises adjusting at leastone of a scan speed, a laser power, a laser scan path, a spot size, or arate of heating or cooling a material of the deposited or fused one ormore layers.
 22. The method of claim 20, wherein the one or moremeasured parameters includes at least one of a temperature distribution,emissivity, band ratios, radiation transport characteristics, or a meltpool shape.
 23. The method of claim 20, wherein processing the one ormore measured parameters using the controller to determine if anyadjustments to the deposited or fused one or more layers are desirablefurther comprises at least one of: determining statistical moments ofthe one or more parameters; extracting at least one of spatialderivatives, temporal derivatives, or spectral derivatives; andprocessing the at least one of spatial derivatives, temporalderivatives, or spectral derivatives to generate at least one of aquality control assessment or a process control signal of the depositedor fused one or more layers.
 24. (canceled)
 25. The method of claim 20,further comprising performing temperature-emissivity separation on theone or more measured parameters, the one or more measured parameterscomprising one or more spectrally resolved radiance measurements. 26.The method of claim 25, wherein the one or more measured parameterscomprises a first spectrally resolved radiance measurement and a secondspectrally resolved radiance measurement, and the temperature-emissivityseparation is performed using a two temperature method.
 27. (canceled)28. The method of claim 20, wherein the one or more measured parameterscomprises emissivity data, the method further comprising qualifying thedimensions of the three-dimensional object in view of the emissivitydata.
 29. The method of claim 20, wherein the one or more measuredparameters comprises anomalous spectral features, the method furthercomprising assessing parameters of a melt pool of the additivemanufacturing printer in view of the anomalous spectral features. 30.The method of claim 20, wherein communicating commands to the additivemanufacturing printer to institute the one or more adjustments to theone or more deposited or fused layers prior to depositing or fusing alllayers of the plurality of layers of the three-dimensional objectcomprises at least one of the following: altering one or more laser scanparameters of the additive manufacturing printer to achieve at least oneof a desirable thermal gradient or a time derivative temperature withina defined region of the three-dimensional object; selectively re-heatingat least a portion of the one or more deposited or fused layers toachieve at least one of a desirable thermal profile, thermal gradient,or a time derivative of temperature within a defined region of thethree-dimensional object; or adjusting a temperature of at least one ofa melt pool of the additive manufacturing printer or at least a portionof the one or more deposited or fused layers to alter at least one of amicrostructure, residual stress, or surface roughness of a definedregion of the three-dimensional object.
 31. The method of claim 20,wherein communicating commands to the additive manufacturing printer toinstitute the one or more adjustments to the one or more deposited orfused layers prior to depositing or fusing all layers of the pluralityof layers of the three-dimensional object comprises adjusting an energyprofile of a first laser of the additive manufacturing printer tomaintain a desired temperature gradient about a portion of the one ormore deposited or fused layers that is melted by a second laser of theadditive manufacturing printer.
 32. The method of claim 20, whereincommunicating commands to the additive manufacturing printer toinstitute the one or more adjustments to the one or more deposited orfused layers prior to depositing or fusing all layers of the pluralityof layers of the three-dimensional object comprises controlling at leastone of a power or a scan pathway of a laser of the additivemanufacturing printer to provide substantially constant temperaturealong a desired region of a melt pool of the additive manufacturingprinter as a beam of the additive manufacturing printer scans.
 33. Themethod of claim 20, wherein communicating commands to the additivemanufacturing printer to institute the one or more adjustments to theone or more deposited or fused layers prior to depositing or fusing alllayers of the plurality of layers of the three-dimensional objectcomprises performing at least one of adaptive feed-forward control orfeedback control to adjust an energy profile of one or more processlasers of the additive manufacturing printer.
 34. The method of claim33, wherein at least one of adaptive feed-forward control or feedbackcontrol uses previously recorded process data to perform the control.35. The method of claim 33, further comprising operating at least one ofa CAD code or an analysis of corresponding machine code to generatecontrol signals in conjunction with adjusting the energy profile of theone or more process lasers of the additive manufacturing printer. 36.The method of claim 20, wherein communicating commands to the additivemanufacturing printer to institute the one or more adjustments to theone or more deposited or fused layers prior to depositing or fusing alllayers of the plurality of layers of the three-dimensional objectcomprises remelting material included as part of the one or moredeposited or fused layers.
 37. The method of claim 20, whereincommunicating commands to the additive manufacturing printer toinstitute the one or more adjustments to the one or more deposited orfused layers prior to depositing or fusing all layers of the pluralityof layers of the three-dimensional object comprises terminatingmanufacture of the three-dimensional object prior to depositing orfusing all layers of the plurality of layers of the three-dimensionalobject in response to a defect detected by way of the one or moremeasured parameters.
 38. (canceled)
 39. A method for additivemanufacturing, comprising: depositing or fusing one or more layers of aplurality of layers of a three-dimensional object, the depositing orfusing being performed by an additive manufacturing printer; measuringone or more parameters associated with the one or more layers using animaging spectrometer, the measuring occurring prior to depositing orfusing all layers of the plurality of layers of the three-dimensionalobject; and communicating the one or more measured parameters to astorage unit.